Adaptive potential Merge genetic data

1 Load libraries and source code

rm(list=ls())

library(ggplot2)
require(data.table)
library(dplyr)
library(tidyr)
require(parallel)
library(bdc)
library(taxadb)
library(traitdataform)
library(pbapply)
library(tidyverse)
library(readxl)
library(lme4)
library(coefplot)
library(sjPlot)
library(sjmisc)
library(effects)
library(rgdal)
library(maptools)
library(rgeos)
library(terra)
library(MuMIn)
library(rnaturalearthdata)
library(lsmeans)
library(GGally)
library(tidyterra)

2 Functions

# find accepted species names
source(here::here("R/clean_taxa_functions.r"))

# Malin's transformation for moving values slightly inward. 
transform01 <- function(x) (x * (length(x) - 1) + 0.5) / (length(x))

# De Kort transformation
deKort_trans <- function(p){
    p <- scale(p)
    p <- (p - min(p, na.rm = T))/(max(p, na.rm = T)-min(p, na.rm = T))
    return(p)
}

# Malin's transformation before a logit transformation
logit_trans <- function(p){
    p <- transform01(p)
    p <- log(p/(1-p))
    return(p)
}

#function to select random effect
testSingAl=function(x,f,data,n=1:length(x)){
    #function testing for random effect structure
    #x=set of variables to test as random effect
    #f=formula of the fixed structure of the model
    #data= data base with all variables
    #n= levels of variable combination to test (start from 1 when signle variable are tested to the size of the set of variables to test in combination)
    b=1
    if(is.null(x)==F){
        for(i in n){
            v1=combn(x,i)
            if(i>1){
                v2=apply(t(v1),1,paste,sep="",collapse="+")
            }else{
                v2=t(v1)[,1]
            }
            for(j in 1:length(v2)){
                print(paste(i," : ",j,sep=""))
                f1=paste(f,"+",v2[j],sep="")
                lme1=lmer(as.formula(f1),data,REML=F,na.action="na.fail",control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb')))
                a=summary(lme1)$optinfo$conv$lme4$messages
                if(is.null(a)==F){
                    a=paste(a,sep="",collapse="_")
                }else{
                    a=NA
                }
                res=data.frame(test=v2[j],R2m=r.squaredGLMM(lme1)[[1]],R2c=r.squaredGLMM(lme1)[[2]],aic=AIC(lme1)[[1]],aicc=AICc(lme1)[[1]],singular=isSingular(lme1)[[1]],nV=i,source=grep("Source",v2[j])[1],warning=a[[1]])
                if(b==1){
                    resOK=res
                }else{
                    resOK=rbind(resOK,res)
                }
                b=b+1
            }
        }
    }else{
        f1=f
        lme1=lmer(as.formula(f1),data,REML=F,na.action="na.fail",control = lmerControl(optimizer ='optimx', optCtrl=list(method='nlminb')))
        a=summary(lme1)$optinfo$conv$lme4$messages
        if(is.null(a)==F){
            a=paste(a,sep="",collapse="_")
        }else{
            a=NA
        }
        resOK=data.frame(test=f,R2m=r.squaredGLMM(lme1)[[1]],R2c=r.squaredGLMM(lme1)[[2]],aic=AIC(lme1)[[1]],aicc=AICc(lme1)[[1]],singular=isSingular(lme1)[[1]],warning=a[[1]])
        
    }
    
    return(resOK)
    #output:
    #test= random structure
    #R2m= marginal R2 of the model
    #R2c= conditional R2 of the model
    #aic= Akaike information criterion of the model
    #aicc= Akaike information criterion with small-sample correction
    #singular= output of the model singularity test (FALSE= no signularity; TRUE= singularity issue)
    #nV= number of variables tested as random effect
    #warning= inform for warning during the model fit (such as convergence issue)
}

3 Load species list

splist <- read.csv(here::here("Data/splist.csv"), header = T)

# remove duplicated sp_names
splist <- splist %>%
    dplyr::filter(!duplicated(scientificName))%>%
    mutate(Kingdom=kingdom,
           Phylum=phylum,
           Class=class,
           Order=order,
           Family=family)%>%
    dplyr::select(scientificName,Kingdom,Phylum,Class,Order,Family,db)

splist$Genus <- sapply(splist$scientificName, function(x){
    strsplit(x," ")[[1]][1]
})

4 Load bioshifts

4.1 Load v1

biov1 <- read.csv(here::here("Data/biov1_fixednames.csv"), header = T)

# Fix references in biov1
biov1$Article_ID <- gsub("[^0-9.-]", "", biov1$Article_ID)
biov1$Article_ID <- as.numeric(biov1$Article_ID)
biov1$sp_name_std_v1 <- gsub("_"," ",biov1$sp_name_std_v1)
biov1 <- biov1 %>%
    dplyr::select(ID,Article_ID,Study_ID,
                  Type,Param,Trend,SHIFT,UNIT,DUR,
                  v.lat.mean,v.ele.mean,
                  START,END,Sampling,Uncertainty_Distribution,Uncertainty_Parameter,
                  N,Grain_size,Data,ID.area,
                  Phylum,Class,Order,Family,Genus,sp_name_std_v1,
                  group,ECO,Hemisphere) %>% # select columns
    mutate(
        Type = case_when(
            Type=="HOR" ~ "LAT",
            TRUE ~ as.character(Type)),
        Data = case_when(
            Data=="occurence-based" ~ "occurrence-based",
            TRUE ~ as.character(Data)),
        spp = sp_name_std_v1,
        SHIFT_abs = abs(SHIFT),
        velocity = ifelse(Type == "LAT", v.lat.mean, v.ele.mean),
        vel_sign = ifelse(velocity>0,"pos","neg"))

biov1 <- biov1 %>%
    dplyr::filter(!is.na(sp_name_std_v1))

all(biov1$sp_name_std_v1 %in% splist$scientificName)

## [1] TRUE

# from continuous to categorical
q1=quantile(biov1$START,probs=c(0,0.25,0.5,0.75,1))
biov1$StartF=cut(biov1$START,breaks=q1,include.lowest=T)

q1=quantile(biov1$ID.area,probs=c(0,0.25,0.5,0.75,1))
biov1$AreaF=cut(biov1$ID.area,breaks=q1,include.lowest=T)

q1=quantile(biov1$N,probs=c(0,0.25,0.5,0.75,1))
biov1$NtaxaF=cut(biov1$N,breaks=q1,include.lowest=T)

# add ID to obs
biov1$obs_ID <- paste0("S",1:nrow(biov1))


summary(biov1$velocity)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## -5.8328  0.5415  1.9642  2.2635  2.7543 14.5492    1450

summary(biov1$velocity[which(biov1$vel_sign=="pos")])

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.009396  0.541507  2.055833  2.411912  2.831932 14.549230

4.2 Classify species

# Class species as Terrestrial, Marine or Freshwater
Terv1 <- unique(biov1$sp_name_std_v1[which(biov1$ECO == "T")])
Terrestrials = unique(c(Terv1))

Mar1 <- unique(biov1$sp_name_std_v1[which(biov1$ECO == "M")])
Marine = unique(c(Mar1))

# Freshwater fish
FFishv1 <- unique(biov1$sp_name_std_v1[(biov1$Class == "Actinopterygii" | biov1$Class == "Cephalaspidomorphi") & biov1$ECO == "T"])
FFish = unique(c(FFishv1))
# Marine fish
MFishv1 <- unique(biov1$sp_name_std_v1[biov1$Class == "Actinopterygii" & biov1$ECO == "M"])
MFish = unique(c(MFishv1))

splist$ECO = NA
splist$ECO[which(splist$scientificName %in% Terrestrials)] <- "T"
splist$ECO[which(splist$scientificName %in% Marine)] <- "M"
splist$ECO[which(splist$scientificName %in% MFish)] <- "M"

biov1$ECO = NA
biov1$ECO[which(biov1$sp_name_std_v1 %in% Terrestrials)] <- "T"
biov1$ECO[which(biov1$sp_name_std_v1 %in% Marine)] <- "M"
biov1$ECO[which(biov1$sp_name_std_v1 %in% MFish)] <- "M"

splist$Group = NA
splist$Group[which(splist$Class == "Phaeophyceae")] <- "Chromista"
splist$Kingdom[which(splist$Class == "Phaeophyceae")] <- "Chromista"
splist$Group[which(splist$Phylum == "Rhodophyta")] <- "Seaweed"
splist$Kingdom[which(splist$Phylum == "Rhodophyta")] <- "Plantae"
splist$Group[which(splist$Family == "Elminiidae")] <- "Barnacles"
splist$Group[which(splist$Kingdom == "Bacteria")] <- "Bacteria"
splist$Group[which(splist$Class == "Holothuroidea")] <- "Sea cucumber"
splist$Group[which(splist$Class == "Aves")] <- "Bird"
splist$Group[which(splist$Class == "Insecta")] <- "Insect"
splist$Group[which(splist$Class == "Mammalia")] <- "Mammal"
splist$Group[which(splist$Class == "Arachnida")] <- "Spider"
splist$Kingdom[which(splist$Kingdom == "Viridiplantae")] <- "Plantae"
splist$Kingdom[which(splist$Phylum == "Tracheophyta")] <- "Plantae"
splist$Group[which(splist$Kingdom == "Plantae")] <- "Plant"
splist$Group[which(splist$Class == "Hydrozoa")] <- "Hydrozoa"
splist$Group[which(splist$Class == "Anthozoa")] <- "Sea anemones and corals"
splist$Group[which(splist$Class == "Polychaeta")] <- "Polychaetes"
splist$Group[which(splist$Phylum == "Mollusca")] <- "Molluscs"
splist$Group[which(splist$Class == "Malacostraca")] <- "Crustacean"
splist$Group[which(splist$Class == "Hexanauplia")] <- "Crustacean"
splist$Group[which(splist$Class == "Maxillopoda")] <- "Crustacean"
splist$Group[which(splist$Class == "Ostracoda")] <- "Crustacean"
splist$Group[which(splist$Class == "Branchiopoda")] <- "Crustacean"
splist$Group[which(splist$Class == "Asteroidea")] <- "Starfish"
splist$Group[which(splist$Class == "Ascidiacea")] <- "Ascidians tunicates and sea squirts"
splist$Class[which(splist$Class == "Actinopteri")] <- "Actinopterygii"
splist$Group[which(splist$Class == "Actinopterygii")] <- "Fish"
splist$Group[which(splist$Class == "Elasmobranchii")] <- "Fish"
splist$Group[which(splist$Order == "Perciformes")] <- "Fish"
splist$Group[which(splist$Class == "Chondrichthyes")] <- "Fish"
splist$Group[which(splist$Class == "Holocephali")] <- "Fish"
splist$Group[which(splist$Class == "Cephalaspidomorphi")] <- "Fish"
splist$Group[which(splist$Class == "Echinoidea")] <- "Sea urchin"
splist$Group[which(splist$Class == "Crinoidea")] <- "Crinoid"
splist$Group[which(splist$Class == "Holothuroidea")] <- "Sea cucumber"
splist$Group[which(splist$Class == "Reptilia")] <- "Reptile"
splist$Group[which(splist$Order == "Squamata")] <- "Reptile"
splist$Group[which(splist$Class == "Ophiuroidea")] <- "Brittle stars"
splist$Group[which(splist$Class == "Chilopoda")] <- "Centipedes"
splist$Group[which(splist$Class == "Diplopoda")] <- "Millipedes"
splist$Group[which(splist$Class == "Amphibia")] <- "Amphibian"
splist$Group[which(splist$Kingdom == "Fungi")] <- "Fungi"
splist$Group[which(splist$Order == "Balanomorpha")] <- "Barnacles"
splist$Group[which(splist$Phylum == "Nematoda")] <- "Nematodes"
splist$Group[which(splist$Class == "Myxini")] <- "Hagfish"
splist$Group[which(splist$Kingdom == "Chromista")] <- "Chromista"
splist$Family[which(splist$Genus == "Dendrocopus")] <- "Picidae"

######################################
biov1 <- merge(biov1[,-which(names(biov1) %in% c("Phylum","Class","Order","Family","Genus","Group","ECO"))], 
               splist[,c("Kingdom","Phylum","Class","Order","Family","Genus","Group","ECO","scientificName")], 
               by.x = "sp_name_std_v1", by.y = "scientificName", 
               all.x = T)

4.3 Filter LAT / ELE shifts

Use only latitudinal and elevational shifts

# v1
biov1 <- biov1 %>%
    dplyr::filter(Type %in% c("ELE","LAT"))  # Use LAT ELE shifts

# splist
sps <- unique(biov1$sp_name)
splist <- splist %>% dplyr::filter(scientificName %in% sps)

4.4 Remove species identified to the genus level or cf.

if(any(grep("sp[.]",biov1$sp_reported_name_v1))){
    biov1 <- biov1 %>% dplyr::filter(!grepl("sp[.]",sp_reported_name_v1))
}
if(any(grep("sp[.]",biov1$sp_name_std_v1))){
    biov1 <- biov1 %>% dplyr::filter(!grepl("sp[.]",sp_name_std_v1))
}
if(any(grep("cf[.]",biov1$sp_reported_name_v1))){
    biov1 <- biov1 %>% dplyr::filter(!grepl("cf[.]",sp_reported_name_v1))
}
if(any(grep("cf[.]",biov1$sp_name_std_v1))){
    biov1 <- biov1 %>% dplyr::filter(!grepl("cf[.]",sp_name_std_v1))
}

#remove freshwater fishes
biov1 <- biov1[-which((biov1$Class == "Actinopterygii" | biov1$Group == "Fish") & biov1$ECO=="T"),]

#remove marine birds
biov1 <- biov1[-which(biov1$Class == "Aves" & biov1$ECO=="M"),]


if(any(grep("sp[.]",splist$scientificName))){
    splist <- splist %>% dplyr::filter(!grepl("sp[.]",scientificName))
}
if(any(grep("sp[.]",splist$scientificName))){
    splist <- splist %>% dplyr::filter(!grepl("sp[.]",scientificName))
}
if(any(grep("cf[.]",splist$scientificName))){
    splist <- splist %>% dplyr::filter(!grepl("cf[.]",scientificName))
}
if(any(grep("cf[.]",splist$scientificName))){
    splist <- splist %>% dplyr::filter(!grepl("cf[.]",scientificName))
}

4.5 Reclassify methodological variables

unique(biov1$Data)

## [1] "abundance-based"  "occurrence-based"

biov1$Data[biov1$Data=="occurence-based"] <- "occurrence-based"
biov1$Data <- factor(biov1$Data, levels = unique(biov1$Data))
table(biov1$Data)

## 
##  abundance-based occurrence-based 
##             9496            20744

unique(biov1$Sampling)

## [1] "TWO"                  "CONTINUOUS"           "MULTIPLE(continuous)"
## [4] "IRR"

biov1$Sampling = ifelse(biov1$Sampling %in% c("IRR","MULTIPLE(continuous)"),"MULTIPLE", biov1$Sampling)
biov1$Sampling <- ordered(biov1$Sampling, 
                          levels = c("TWO","MULTIPLE","CONTINUOUS"))
table(biov1$Sampling)

## 
##        TWO   MULTIPLE CONTINUOUS 
##      25819       1251       3170

unique(biov1$Grain_size)

## [1] "small"      "large"      "moderate"   "very_large" NA

biov1$Grain_size <- ifelse(biov1$Grain_size %in% c("large","very_large"),"large",biov1$Grain_size)
biov1$Grain_size <- ordered(biov1$Grain_size, 
                            levels = c("small","moderate","large"))
table(biov1$Grain_size)

## 
##    small moderate    large 
##     9841    10971     9427

unique(biov1$Uncertainty_Distribution)

## [1] "RESAMPLING(same)"               "RAW"                           
## [3] "RESAMPLING"                     "MODEL"                         
## [5] "RESAMPLING+MODEL"               "MODEL+RESAMPLING(same)"        
## [7] "RESAMPLING(same)+DETECTABILITY" "RESAMPLING(same)+MODEL"        
## [9] "DETECTABILITY"

biov1$Uncertainty_Distribution <- ifelse(biov1$Uncertainty_Distribution %in% c("RESAMPLING","RESAMPLING(same)"),"RESAMPLING",
                                         ifelse(biov1$Uncertainty_Distribution %in% c("MODEL","MODEL+RESAMPLING(same)","RESAMPLING+MODEL"),"MODEL",
                                                ifelse(biov1$Uncertainty_Distribution %in% c("DETECTABILITY","RESAMPLING(same)+DETECTABILITY"),"DETECTABILITY",
                                                       biov1$Uncertainty_Distribution)))
biov1$Uncertainty_Distribution <- ifelse(biov1$Uncertainty_Distribution == "RAW","OPPORTUNISTIC","PROCESSED")  
table(biov1$Uncertainty_Distribution)

## 
## OPPORTUNISTIC     PROCESSED 
##          9532         20708

#transform study area
biov1$ID.area <- log(biov1$ID.area)

4.6 Get centroids of study areas

This will be used to merge genetic data with bioshifts based on distance of observations

# # The input file geodatabase
# fgdb <- "C:/Users/brunn/NextCloud/bioshifts_v1_v2/v1/Study_Areas_v1/Study_Areas.gdb"
# 
# # List all feature classes in a file geodatabase
# fc_list <- ogrListLayers(fgdb)
# 
# # Get centroids
# cl <- makeCluster(detectCores()-1)
# clusterExport(cl, c("readOGR", "gCentroid", "fgdb"))
# 
# centroids <- pblapply(fc_list, function(x){
#     fc <- readOGR(dsn=fgdb,layer=x)
#     c <- data.frame(gCentroid(fc))
#     names(c) <- c("centroid.x", "centroid.y")
# 
#     geomet <- data.frame(terra::geom(terra::vect(fc)))
# 
#     max_lat <- order(geomet[,"y"], decreasing = T)[1]
#     max_lat <- geomet[max_lat,c("x","y")]
#     names(max_lat) <- c("max_lat.x", "max_lat.y")
# 
#     min_lat <- order(geomet[,"y"])[1]
#     min_lat <- data.frame(geomet[min_lat,c("x","y")])
#     names(min_lat) <- c("min_lat.x", "min_lat.y")
# 
#     cbind(c, max_lat, min_lat)
# }, cl = cl)
# 
# stopCluster(cl)
# 
# centroids <- do.call("rbind",centroids)
# centroids$ID <- fc_list
# 
# write.csv(centroids, "Data/centroids_study_areas.csv", row.names = FALSE)

centroids <- read.csv(here::here("Data/centroids_study_areas.csv"))

biov1 <- merge(biov1, centroids, by = "ID")

5 Corrected shifts

Following the approach proposed by Romain Bertrand

Just load it in

biov1 <- read.csv("Data/biov1_corrected_shifts.csv")

6 Direction of shift

Identify direction of shift

biov1 <- biov1 %>%
    mutate(shift_sign = ifelse(SHIFT>0,"pos","neg"),
           shift_vel_sign = paste0(shift_sign,vel_sign),
           shiftC_sign = ifelse(SHIFT_cor>0,"pos","neg"),
           shiftC_vel_sign = paste0(shiftC_sign,vel_sign)
           ) %>%
    filter(!is.na(velocity))

ggplot(biov1, aes(x=shift_vel_sign))+
    geom_bar()+
    coord_flip()+
    facet_wrap(Type~Param)

ggplot(biov1, aes(x=shiftC_vel_sign))+
    geom_bar()+
    coord_flip()+
    facet_wrap(Type~Param)

summary(biov1$velocity)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -5.8328  0.5415  1.9642  2.2647  2.6421 14.5492

summary(biov1$velocity[which(biov1$shift_vel_sign=="pospos")])

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.009396  0.541507  2.055833  2.462087  2.831932 14.549230

summary(biov1$velocity[which(biov1$shift_vel_sign=="negneg")])

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -3.79347 -2.30487 -1.78004 -1.60769 -0.58152 -0.02945

7 Calculate lags raw

# calculate lags
# positive values are a lag (range shift lower smaller than expected or in opposite directions) and negative values are range shift larger than expected
biov1$lag <- biov1$velocity-biov1$SHIFT
position = which(biov1$vel_sign == "neg")
biov1$lag[position] <- biov1$lag[position] * -1 # Any negative velocity means the sign of lag has to shift.

# Lag 2
# When shift is in opposite sign of velocity, lag = abs(velocity)
biov1$lag2 = biov1$lag
position <- which(biov1$shift_vel_sign == "posneg" | biov1$shift_vel_sign == "negpos")
biov1$lag2[position] <- abs(biov1$velocity[position])

# Lag 3
# When shift is > velocity, lag = 0
biov1$lag3 = biov1$lag2
position <- which((biov1$shift_sign == "pos" & biov1$SHIFT > biov1$velocity) |
                      (biov1$shift_sign == "neg" & biov1$SHIFT < biov1$velocity))
biov1$lag3[position] <- 0


{
    par(mfrow=c(2,2))
    plot(lag~velocity, biov1)
    plot(lag2~velocity, biov1)
    plot(lag3~velocity, biov1)
}

ggplot(biov1, aes(x = Param, y=lag, fill = Param, color = Param))+
    geom_boxplot(alpha = .5, outlier.shape = NA)+
    scale_y_continuous(limits = quantile(biov1$lag2, c(0.1, 0.9), na.rm = T))+
    facet_wrap(.~Type, scales = "free")

## Warning: Removed 8756 rows containing non-finite values (`stat_boxplot()`).

ggplot(biov1, aes(x = Param, y=lag2, fill = Param, color = Param))+
    geom_boxplot(alpha = .5, outlier.shape = NA)+
    scale_y_continuous(limits = quantile(biov1$lag2, c(0.1, 0.9), na.rm = T))+
    facet_wrap(.~Type, scales = "free")

## Warning: Removed 6006 rows containing non-finite values (`stat_boxplot()`).

ggplot(biov1, aes(x = Param, y=log1p(lag3), fill = Param, color = Param))+
    geom_boxplot(alpha = .5, outlier.shape = NA)+
    # scale_y_continuous(limits = quantile(log1p(biov1$lag3), c(0.1, 0.9), na.rm = T))+
    facet_wrap(.~Type, scales = "free")

8 Calculate lags corrected

# calculate lagCs
# positive values are a lagC (range shift lower smaller than expected or in opposite directions) and negative values are range shift larger than expected
biov1$lagC <- biov1$velocity-biov1$SHIFT_cor
position = which(biov1$vel_sign == "neg")
biov1$lagC[position] <- biov1$lagC[position] * -1 # Any negative velocity means the sign of lagC has to shift.

# Lag 2
# When shift is in opposite sign of velocity, lagC == velocity
biov1$lagC2 = biov1$lagC
position <- which(biov1$shiftC_vel_sign == "posneg" | biov1$shiftC_vel_sign == "negpos")
biov1$lagC2[position] <- abs(biov1$velocity[position])

# Lag 3
# When shift is > velocity, lagC = 0
biov1$lagC3 = biov1$lagC2
position <- which((biov1$shiftC_sign == "pos" & biov1$SHIFT_cor > biov1$velocity) |
                      (biov1$shiftC_sign == "neg" & biov1$SHIFT_cor < biov1$velocity))
biov1$lagC3[position] <- 0


{
    par(mfrow=c(2,2))
    plot(lagC~velocity, biov1)
    plot(lagC2~velocity, biov1)
    plot(lagC3~velocity, biov1)
}

ggplot(biov1, aes(x = Param, y=lagC, fill = Param, color = Param))+
    geom_boxplot(alpha = .5, outlier.shape = NA)+
    scale_y_continuous(limits = quantile(biov1$lagC2, c(0.1, 0.9), na.rm = T))+
    facet_wrap(.~Type, scales = "free")

## Warning: Removed 6070 rows containing non-finite values (`stat_boxplot()`).

ggplot(biov1, aes(x = Param, y=lagC2, fill = Param, color = Param))+
    geom_boxplot(alpha = .5, outlier.shape = NA)+
    scale_y_continuous(limits = quantile(biov1$lagC2, c(0.1, 0.9), na.rm = T))+
    facet_wrap(.~Type, scales = "free")

## Warning: Removed 6012 rows containing non-finite values (`stat_boxplot()`).

ggplot(biov1, aes(x = Param, y=log1p(lagC3), fill = Param, color = Param))+
    geom_boxplot(alpha = .5, outlier.shape = NA)+
    # scale_y_continuous(limits = quantile(log1p(biov1$lagC3), c(0.1, 0.9), na.rm = T))+
    facet_wrap(.~Type, scales = "free")

9 Corrected vs raw shifts

plot(SHIFT_cor~SHIFT,biov1)
abline(a=0,b=1,col=2)

lm0=lm(SHIFT_cor~SHIFT,biov1)
summary(lm0)

## 
## Call:
## lm(formula = SHIFT_cor ~ SHIFT, data = biov1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19.6130  -0.1863   0.0470   0.1971  10.9133 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.1815049  0.0028742  -63.15   <2e-16 ***
## SHIFT        0.1556612  0.0005166  301.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4872 on 30055 degrees of freedom
## Multiple R-squared:  0.7513, Adjusted R-squared:  0.7513 
## F-statistic: 9.08e+04 on 1 and 30055 DF,  p-value: < 2.2e-16

hist(biov1$SHIFT,col=rgb(1,0,0,0.5))
hist(biov1$SHIFT_cor,col=rgb(0,0,1,0.5),add=T)

#so by using residuals we decrease the variation in the raw range shift obs, it's like we work on a more homogenous variables as all the variations due to methods variation has been substracted to the shift.

x1=tapply(biov1$SHIFT,biov1$Group,mean)
x2=tapply(biov1$SHIFT_cor,biov1$Group,mean)#lower mean value than true obs 
plot(x1~x2, xlab="Mean shift per Group (raw)", ylab="Mean shift per Group (corrected)")

lm0=lm(x1~x2,biov1)
summary(lm0) #it changes many things

## 
## Call:
## lm(formula = x1 ~ x2, data = biov1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5194 -1.0516 -0.2973  0.4605  3.3886 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2.1183     0.3504   6.045 2.24e-05 ***
## x2            5.6218     2.7006   2.082   0.0549 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.418 on 15 degrees of freedom
## Multiple R-squared:  0.2241, Adjusted R-squared:  0.1724 
## F-statistic: 4.333 on 1 and 15 DF,  p-value: 0.05491

x1=tapply(biov1$SHIFT,biov1$Group,var)
x2=tapply(biov1$SHIFT_cor,biov1$Group,var)#lower variance value than in true obs 
plot(x1~x2, xlab="Variance shift per Group (raw)", ylab="Variance shift per Group (corrected)")

lm0=lm(x1~x2,biov1)
summary(lm0) #but high positive correlation meaning that relative variation is conserved

## 
## Call:
## lm(formula = x1 ~ x2, data = biov1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.801 -19.391  -9.706  -1.438  71.182 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   22.755      8.042   2.829   0.0127 *  
## x2            17.645      2.262   7.801 1.17e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 29.41 on 15 degrees of freedom
## Multiple R-squared:  0.8022, Adjusted R-squared:  0.7891 
## F-statistic: 60.85 on 1 and 15 DF,  p-value: 1.173e-06

x1=tapply(biov1$SHIFT,biov1$sp_name_std_v1,mean)
x2=tapply(biov1$SHIFT_cor,biov1$sp_name_std_v1,mean)#lower mean value than in true obs 
plot(x1~x2, xlab="Mean shift per species (raw)", ylab="Mean shift per species (corrected)")

lm0=lm(x1~x2,biov1)
summary(lm0) #but at the species level, we observe a high positive relationship so the corrected shifts did not change the pattern of range shift=> good

## 
## Call:
## lm(formula = x1 ~ x2, data = biov1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.809  -1.107  -0.470   0.569 129.850 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.41199    0.02744   51.47   <2e-16 ***
## x2           5.04851    0.03134  161.08   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.02 on 12118 degrees of freedom
## Multiple R-squared:  0.6817, Adjusted R-squared:  0.6816 
## F-statistic: 2.595e+04 on 1 and 12118 DF,  p-value: < 2.2e-16

#looking at the relationship with climate velocity
#WARNING:here you need to adapt the code: if you look at the elevational range shift so you have to use EleVeloT in order to use the corresponding climate velocity
par(mfrow=c(1,2))
plot(SHIFT~velocity, xlab="SHIFT (raw)", ylab="Velocity",biov1)
lm0=lm(SHIFT~velocity+I(velocity^2),biov1)
summary(lm0)

## 
## Call:
## lm(formula = SHIFT ~ velocity + I(velocity^2), data = biov1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -120.794   -1.642   -0.792    1.232  145.242 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.849929   0.049121  17.303  < 2e-16 ***
## velocity       0.195422   0.028720   6.804 1.03e-11 ***
## I(velocity^2) -0.010881   0.002856  -3.810 0.000139 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.434 on 30054 degrees of freedom
## Multiple R-squared:  0.002526,   Adjusted R-squared:  0.00246 
## F-statistic: 38.06 on 2 and 30054 DF,  p-value: < 2.2e-16

x1=seq(min(biov1$velocity,na.rm = T),max(biov1$velocity,na.rm = T),le=100)
p1=predict(lm0,newdata=data.frame(velocity=x1),type="response")
points(p1~x1,type="l",col=2,lwd=2)
plot(SHIFT_cor~velocity, xlab="SHIFT (corrected)", ylab="Velocity",biov1)#the pattern is changing a lot
lm0=lm(SHIFT_cor~velocity+I(velocity^2),biov1)
summary(lm0)

## 
## Call:
## lm(formula = SHIFT_cor ~ velocity + I(velocity^2), data = biov1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.1763  -0.3541  -0.0312   0.2835  18.5895 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -0.0091560  0.0088312  -1.037  0.29985   
## velocity       0.0115201  0.0051635   2.231  0.02568 * 
## I(velocity^2) -0.0014568  0.0005135  -2.837  0.00456 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9769 on 30054 degrees of freedom
## Multiple R-squared:  0.0002878,  Adjusted R-squared:  0.0002213 
## F-statistic: 4.326 on 2 and 30054 DF,  p-value: 0.01322

x1=seq(min(biov1$velocity,na.rm = T),max(biov1$velocity,na.rm = T),le=100)
p1=predict(lm0,newdata=data.frame(velocity=x1),type="response")
points(p1~x1,type="l",col=2,lwd=2)

#we observed a relationship for the raw range shift (an unimodal relationship with higher shifts when absolute climate velocity increase).
#for the range shift corrected by method, no relationship with climatic velocity is observed

#looking at the relationship with lag
par(mfrow=c(1,2))
plot(lagC~velocity,biov1,main="corrected lag estimate")
# Lag C2
# When shift is in opposite sign of velocity, lag == velocity
plot(lagC2~velocity,biov1,main="corrected lag estimate with correction for special case")

#change more things than the above lag metrics, still the highly change are observed at extreme negative values

plot(lag2~lagC2,biov1)
lm0=lm(lag~lagC,biov1)
summary(lm0) #we observe a high positive relationship so the corrected lag did not change the pattern of the observed lag=> good

## 
## Call:
## lm(formula = lag ~ lagC, data = biov1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -140.499   -1.276    0.724    1.869   96.858 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2.056745   0.034810  -59.09   <2e-16 ***
## lagC         1.414888   0.009807  144.27   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.509 on 30055 degrees of freedom
## Multiple R-squared:  0.4092, Adjusted R-squared:  0.4091 
## F-statistic: 2.081e+04 on 1 and 30055 DF,  p-value: < 2.2e-16

par(mfrow=c(1,2))

plot(lag2~velocity,biov1)
lm0=lm(lag2~velocity+I(velocity^2),biov1)
summary(lm0) #significant; R2=46%

## 
## Call:
## lm(formula = lag2 ~ velocity + I(velocity^2), data = biov1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -144.671   -0.360    1.098    2.100    6.457 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -0.874182   0.037989  -23.01   <2e-16 ***
## velocity       0.273585   0.022211   12.32   <2e-16 ***
## I(velocity^2)  0.054244   0.002209   24.56   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.202 on 30054 degrees of freedom
## Multiple R-squared:  0.191,  Adjusted R-squared:  0.1909 
## F-statistic:  3548 on 2 and 30054 DF,  p-value: < 2.2e-16

x1=seq(min(biov1$velocity, na.rm = T),max(biov1$velocity, na.rm = T),le=100)
p1=predict(lm0,newdata=data.frame(velocity=x1),type="response")
points(p1~x1,type="l",col=2,lwd=2) #bimodal relationship

plot(lagC2~velocity,biov1) 
lm0=lm(lagC2~velocity+I(velocity^2),biov1)
summary(lm0)#significant; R2=86

## 
## Call:
## lm(formula = lagC2 ~ velocity + I(velocity^2), data = biov1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.4839  -0.2248   0.0613   0.3711   7.2808 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2523552  0.0075364   33.48   <2e-16 ***
## velocity      0.6480167  0.0044064  147.06   <2e-16 ***
## I(velocity^2) 0.0305757  0.0004382   69.78   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8337 on 30054 degrees of freedom
## Multiple R-squared:  0.891,  Adjusted R-squared:  0.8909 
## F-statistic: 1.228e+05 on 2 and 30054 DF,  p-value: < 2.2e-16

x1=seq(min(biov1$velocity, na.rm = T),max(biov1$velocity, na.rm = T),le=100)
p1=predict(lm0,newdata=data.frame(velocity=x1),type="response")
points(p1~x1,type="l",col=2,lwd=2) #bimodal relationship

10 All shifts vs same direction

ggplot(biov1, aes(x = velocity, y = SHIFT))+
    geom_point(aes(color = lag2), alpha = .1)+
    scale_color_viridis_c()+
    geom_smooth(method = "lm")+
    theme_classic()+
    facet_wrap(Param~Type+ECO, scales = "free")

## `geom_smooth()` using formula = 'y ~ x'

ggplot(biov1 %>%
           filter(shift_vel_sign == "pospos" | shift_vel_sign == "negneg"), 
       aes(x = abs(velocity), y = abs(SHIFT)))+
    geom_point(aes(color = lag2), alpha = .1)+
    scale_color_viridis_c()+
    geom_smooth(method = "lm")+
    theme_classic()+
    facet_wrap(Param~Type+ECO, scales = "free")

## `geom_smooth()` using formula = 'y ~ x'

ggplot(biov1, aes(x = velocity, y = SHIFT_cor))+
    geom_point(aes(color = lag2), alpha = .1)+
    scale_color_viridis_c()+
    geom_smooth(method = "lm")+
    theme_classic()+
    facet_wrap(Param~Type+ECO, scales = "free")

## `geom_smooth()` using formula = 'y ~ x'

ggplot(biov1 %>%
           filter(shift_vel_sign == "pospos" | shift_vel_sign == "negneg"), 
       aes(x = abs(velocity), y = abs(SHIFT_cor)))+
    geom_point(aes(color = lag2), alpha = .1)+
    scale_color_viridis_c()+
    geom_smooth(method = "lm")+
    theme_classic()+
    facet_wrap(Param~Type+ECO, scales = "free")

## `geom_smooth()` using formula = 'y ~ x'

11 Load genetic data

# Genetic diversity
mt <- fread(here::here('Data/mtdna.csv'))
mt$spp <- Clean_Names(mt$spp)

ms <- fread(here::here('Data/msat.csv'))
ms$spp <- Clean_Names(ms$spp)

gen_d <- fread(here::here('Data/Deposited_data_genetic_diversity_dekort2021.csv'))
gen_d$spp <-  gsub("_"," ",gen_d$Species)
gen_d$spp <- Clean_Names(gen_d$spp)

gen_lf <- read_excel(here::here("Data/MacroPopGen_Database_final-v0.2.xlsx"), sheet = "MacroPopGen_Database")
gen_lf$spp <-  gsub("_"," ",gen_lf$G_s)
gen_lf$spp <- Clean_Names(gen_lf$spp)

# Phenotypic rates
pheno <- fread(here::here('Data/PROCEEDv6_RatesDB.csv'))
pheno$spp <- Clean_Names(pheno$sp_ncbi)

length(unique(gen_d$spp))
length(unique(gen_lf$spp))
length(unique(mt$spp))
length(unique(ms$spp))
length(unique(pheno$spp))

11.1 Fix names at the genetic data

# Species to find
mycols <- c("species","scientificName","kingdom","phylum","class","order","family","db_code")
splist <- data.frame(matrix(ncol = length(mycols), nrow = length(unique(c(gen_sps,phen_sps)))))
names(splist) <- mycols

splist$reported_name_fixed <- unique(c(gen_sps,phen_sps))
tofind_ <- splist[which(is.na(splist$scientificName)),]
tofind_ <- unique(tofind_$reported_name_fixed)

tofind <- data.frame(matrix(nrow = length(tofind_), ncol = 8))
names(tofind) = c("scientificName", "kingdom", "phylum", "class", "order", "family", "db", "db_code")

tofind <- data.frame(species = tofind_, tofind)

tofind <- tofind %>%
    mutate(across(everything(), as.character))

# retrieve sp names
sp_names_found <- Find_Sci_Names(spnames = tofind$species)

# Feed
tofind <- tofind %>% 
    rows_patch(sp_names_found, 
               by = "species")

gc()

## Add found species names to the splist
all(tofind$species %in% splist$reported_name_fixed)

if(any(is.na(tofind$scientificName))){
    found <- tofind[-which(is.na(tofind$scientificName)),]
} else {
    found = tofind
}

for(i in 1:length(found$species)){
    tofill <- unique(which(splist$reported_name_fixed == found$species[i]))
    splist$scientificName[tofill] <- found$scientificName[i]
    splist$kingdom[tofill] <- found$kingdom[i]
    splist$phylum[tofill] <-found$phylum[i]
    splist$class[tofill] <- found$class[i]
    splist$order[tofill] <- found$order[i]
    splist$family[tofill] <- found$family[i]
    splist$db[tofill] <- found$db[i]
    splist$db_code[tofill] <- found$db_code[i]
}

splist$spp = splist$reported_name_fixed

# Keep original names for those species we could not find a name at GBIF
pos <- which(is.na(splist$scientificName))
splist$scientificName[pos] <- splist$spp[pos]

any(!mt$spp %in% gen_sps)
any(!mt$spp %in% splist$spp)

any(!ms$spp %in% gen_sps)
any(!ms$spp %in% splist$spp)

any(!gen_d$spp %in% gen_sps)
any(!gen_d$spp %in% splist$spp)

any(!gen_lf$spp %in% gen_sps)
any(!gen_lf$spp %in% splist$spp)

for(i in 1:nrow(mt)){
    mt$spp_new[i] <- splist$scientificName[which(splist$spp == mt$spp[i])]
}
for(i in 1:nrow(ms)){
    ms$spp_new[i] <- splist$scientificName[which(splist$spp == ms$spp[i])]
}
for(i in 1:nrow(gen_d)){
    gen_d$spp_new[i] <- splist$scientificName[which(splist$spp == gen_d$spp[i])]
}
for(i in 1:nrow(gen_lf)){
    gen_lf$spp_new[i] <- splist$scientificName[which(splist$spp == gen_lf$spp[i])]
}
for(i in 1:nrow(gen_lf)){
    pheno$spp_new[i] <- splist$scientificName[which(splist$spp == pheno$spp[i])]
}

write.csv(mt, 
          here::here('Data/mtdna_harmo.csv'), row.names = FALSE)
write.csv(ms, 
          here::here('Data/msat_harmo.csv'), row.names = FALSE)
write.csv(gen_d, 
          here::here('Data/Deposited_data_genetic_diversity_dekort2021_harmo.csv'), row.names = FALSE)
write.csv(gen_lf, 
          here::here('Data/MacroPopGen_Database_final_areas_Lawrence_Fraser_2020_harmo.csv'), row.names = FALSE)
write.csv(pheno, 
          here::here('Data/PROCEEDv6_RatesDB_harmo.csv'), row.names = FALSE)

12 Load harmonized genetic data

mt <- fread(here::here('Data/mtdna_harmo.csv'))
ms <- fread(here::here('Data/msat_harmo.csv'))
gen_d <- fread(here::here('Data/Deposited_data_genetic_diversity_dekort2021_harmo.csv'))
gen_d$spp <-  gsub("_"," ",gen_d$Species)
gen_lf <- fread(here::here('Data/MacroPopGen_Database_final_areas_Lawrence_Fraser_2020_harmo.csv'))
gen_lf$spp <-  gsub("_"," ",gen_lf$G_s)
pheno <- fread(here::here('Data/PROCEEDv6_RatesDB_harmo.csv'))
pheno$spp <-  gsub("_"," ",pheno$sp_ncbi)

12.1 Stats

# Gen
gen_sps <- unique(c(mt$spp, ms$spp, gen_d$spp, gen_lf$spp))

bsi_v1 <- intersect(biov1$spp, gen_sps)

# Break down
tot1 <- data.frame(N_species_with_gen_data = length(gen_sps),
                   v1_matches = length(bsi_v1))
tot1

##   N_species_with_gen_data v1_matches
## 1                    1894        287

# phenotypic
phen_sps <- unique(pheno$spp)

bsj_v1 <- intersect(biov1$spp, phen_sps)

# Break down
tot2 <- data.frame(N_species_with_pheno_data = length(phen_sps),
                   v1_matches = length(bsj_v1))
tot2

##   N_species_with_pheno_data v1_matches
## 1                       428        260

12.2 Explore distribution

# Malin
ggplot(mt, aes(He)) +
    geom_histogram() +
    ggtitle("Malin's Mitochondrial")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 48 rows containing non-finite values (`stat_bin()`).

ggplot(ms, aes(He)) +
    geom_histogram() +
    ggtitle("Malin's Microsatellite")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# De Kort
ggplot(gen_d, aes(GDp)) +
    geom_histogram() +
    ggtitle("De Kort AFLP & Microsatellite")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Why De Kort data has the two peaks? It is due to the marker type
ggplot(gen_d, aes(GDp)) +
    geom_histogram()+
    facet_grid(.~Marker)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# But if we look at the normalized GDp, the problem is solved. Therefore, we should be using the normalized version of GDp from DeKort
ggplot(gen_d, aes(GDp_norm)) +
    geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Lawrence & Fraser
ggplot(gen_lf, aes(as.numeric(He))) +
    geom_histogram()+
    ggtitle("Lawrence & Fraser Microsatellite")

## Warning in FUN(X[[i]], ...): NAs introduced by coercion

## Warning in FUN(X[[i]], ...): NAs introduced by coercion

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 1283 rows containing non-finite values (`stat_bin()`).

13 Create a single genetic diversity dataset

# Malin
mt_sub <- mt  %>%
    mutate(long = as.numeric(lon),
           lat = as.numeric(lat),
           N = as.numeric(n), # Sample size
           Marker = "Mitochondrial")  %>%
    dplyr::filter(!is.na(He) | !He==0) %>%
    dplyr::select(spp, He, long, lat, Marker)
mt_sub$Source = "Malin"
mt_sub$Het_type = "He"

ms_sub <- ms %>%
    mutate(long = as.numeric(lon),
           lat = as.numeric(lat),
           N = as.numeric(n), # Sample size
           Marker = "Microsatellite")  %>%
    dplyr::filter(!is.na(He) | !He==0) %>%
    dplyr::select(spp, He, long, lat, Marker)
ms_sub$Source = "Malin"
ms_sub$Het_type = "He"

###################
# De Kort

# Jonathan R.:
# co-dominant refers to the fact that you can distinguish homozigous and heterozygous individuals, such as microsatellites
# dominant means that you either have the detection of the marker or not. but you don’t know if the individual is heterozygous or not, such as AFLP
# restriction enzymes are proteins that you use to cut the genomes into fragments. there are used for AFLP and allozymes, so not sure

# De Kort code for Marker:
# CD = Co-dominant
# D = Dominant
# ENZ = Enzime

gen_d_sub <- gen_d %>%
    mutate(long = as.numeric(LONGITUDE),
           lat = as.numeric(LATITUDE),
           spp = spp_new, 
           He = as.numeric(GDp),
           N = as.numeric(SampleSize), # Sample size
           Marker = case_when(
               Marker=="CD" ~ "Microsatellite",
               Marker=="D" ~ "AFLP",
               Marker=="ENZ" ~ "AFLP",
               TRUE ~ as.character(Marker))) %>%
    dplyr::filter(!is.na(He) | !He==0) %>%
    dplyr::filter(!(Marker == "AFLP" & He > .5)) %>% # AFLPs > 0.5 are errors
    dplyr::select(spp, He, long, lat, Marker)
gen_d_sub$Source = "De Kort"
gen_d_sub$Het_type = "He"

# How DeKort calculated the normalized version of GDp
# gen_d_sub$He_harm <- NA
# 
# m <- unique(gen_d_sub$Marker)
# for(i in 1:length(m)){
# pos <- which(gen_d_sub$Marker == m[i])
# gen_d_sub$He_harm[pos] <-  scale(gen_d_sub$He[pos])
# }
# gen_d_sub$He_harm <-  (gen_d_sub$He_harm - min(gen_d_sub$He_harm))/(max(gen_d_sub$He_harm)-min(gen_d_sub$He_harm))

###################
# Lawrence & Fraser
pos <- which(is.na(gen_lf$He))
gen_lf$He_new <- gen_lf$He
gen_lf$He_new[pos] <- gen_lf$Ho[pos]
gen_lf$Het_type = "He"
gen_lf$Het_type[pos] = "Ho"

gen_lf_sub <- gen_lf %>%
    mutate(long = as.numeric(Long),
           lat = as.numeric(Lat),
           He = as.numeric(He_new),
           spp = spp_new,
           N = as.numeric(n), # Sample size
           Marker = "Microsatellite") %>% 
    dplyr::filter(!is.na(He) | !He==0) %>%
    dplyr::select(spp, He, long, lat, Marker, Het_type) 

## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `He = as.numeric(He_new)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

gen_lf_sub$Source = "Lawrence & Fraser"

###################
# Group all
gen_div <- rbind(mt_sub %>% dplyr::select(names(mt_sub)), 
                 ms_sub %>% dplyr::select(names(mt_sub)),
                 gen_d_sub %>% dplyr::select(names(mt_sub)),
                 gen_lf_sub %>% dplyr::select(names(mt_sub)))
gen_div <- na.omit(gen_div)

gen_div$spp <- as.factor(gen_div$spp)
gen_div$Marker <- as.factor(gen_div$Marker)
gen_div$Source <- as.factor(gen_div$Source)

14 Harmonize genetic data

• He_harm: transformation used by DeKort et al 2021 (Nature Communications). Makes markers range from 0-1.

1. scale values at each marker type
2. standardize (x - min)/(max-min)

• He_harm2: transformation suggested by Malin. Applies the function transform01 before a logit transformation.

1. transform01 >> Moves He values slightly inward so that a logit transformation is possible.
2. apply logit transformation

• He_harm3: He_harm2 centered in zero.

gen_div$He_harm <- NA
gen_div$He_harm2 <- NA
gen_div$He_harm3 <- NA

m <- unique(gen_div$Marker)
for(i in 1:length(m)){
    # Select marker type
    pos <- which(gen_div$Marker == m[i])
    # Apply DeKort transformation
    gen_div$He_harm[pos] <- deKort_trans(gen_div$He[pos])
    # Apply logit transformation
    gen_div$He_harm2[pos] <- logit_trans(gen_div$He[pos])
    # Centered in zero
    gen_div$He_harm3[pos] <- scale(gen_div$He_harm2[pos])
}

# Filter the species in Bioshifts
gen_div <- gen_div %>% dplyr::filter(spp %in% unique(biov1$spp))

# remove outliers
gen_metrics <- c("He_harm","He_harm2","He_harm3")

for (i in 1:length(gen_metrics)){
    my_gem <- as.numeric(data.frame(gen_div %>% select(gen_metrics[i]))[,1])
    
    quartiles <- quantile(my_gem, probs=c(.05, .95), na.rm = FALSE)
    IQR <- IQR(my_gem)
    
    Lower <- quartiles[1] - 1.5*IQR
    Upper <- quartiles[2] + 1.5*IQR 
    
    gen_div <- subset(gen_div, my_gem > Lower & my_gem < Upper)
}

14.1 Avg by location

We averaged genetic diversity by location (same species, in the same XY coordinates)

# How many species with >1 gen div measurements at the same site?
# Check how many obs per site and marker type exist
N_obs <- gen_div %>%
    group_by(spp, long, lat) %>%
    tally() %>%
    dplyr::filter(n>1)

DT::datatable(N_obs)

# Is there any species with >1 marker type in the same location?
gen_div %>%
    group_by(spp, long, lat, Marker) %>%
    dplyr::summarise(N = length(unique(spp))) %>%
    dplyr::filter(N>1)

## `summarise()` has grouped output by 'spp', 'long', 'lat'. You can override
## using the `.groups` argument.

## # A tibble: 0 × 5
## # Groups:   spp, long, lat [0]
## # ℹ 5 variables: spp <fct>, long <dbl>, lat <dbl>, Marker <fct>, N <int>

# No, there are not

# Is there any species with >1 Source type in the same location?
gen_div %>%
    group_by(spp, long, lat, Source) %>%
    dplyr::summarise(N = length(unique(spp))) %>%
    dplyr::filter(N>1)

## `summarise()` has grouped output by 'spp', 'long', 'lat'. You can override
## using the `.groups` argument.

## # A tibble: 0 × 5
## # Groups:   spp, long, lat [0]
## # ℹ 5 variables: spp <fct>, long <dbl>, lat <dbl>, Source <fct>, N <int>

# No, there are not

# Avg by site and species
gen_div <- gen_div %>%
    group_by(spp, long, lat) %>%
    dplyr::summarise(He = median(He),
                     He_harm = median(He_harm),
                     He_harm2 = median(He_harm2),
                     He_harm3 = median(He_harm3),
                     Source = paste(Source, sep = ", "),
                     Marker = paste(Marker, sep = ", "))

## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
##   always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `summarise()` has grouped output by 'spp', 'long', 'lat'. You can override
## using the `.groups` argument.

unique(gen_div$Source)

## [1] "De Kort"           "Lawrence & Fraser" "Malin"

unique(gen_div$Marker)

## [1] "AFLP"           "Microsatellite" "Mitochondrial"

14.2 Which species?

gen_div <- merge(gen_div, 
                 splist[,-7],
                 by.x="spp",
                 by.y="scientificName",
                 all.x = T)

toplot_ <- gen_div %>%
    group_by(Group) %>%
    dplyr::summarise(Obs = length(spp),
                     Species = length(unique(spp))) %>%
    gather(var, N, -c(Group))

ggplot(toplot_, aes(x = Group, y = N))+
    ggtitle("N species by Group")+
    geom_bar(stat="identity")+
    geom_text(aes(y=N, label=N, 
                  hjust = ifelse(N<max(N),-.1,1.1)), vjust=0.2, size=3, 
              position = position_dodge(0.9))+
    theme_classic()+
    coord_flip()+
    facet_wrap(.~var, scales = "free", ncol=1)

14.3 Distribution of He

# correlations
ggpairs(gen_div,
        columns = c("He","He_harm","He_harm2","He_harm3"))

ggpairs(gen_div,
        columns = c("He","He_harm","He_harm2","He_harm3"),      
        aes(color = Marker,  
            alpha = 0.5))     

ggpairs(gen_div,
        columns = c("He","He_harm","He_harm2","He_harm3"),      
        aes(color = Source,  
            alpha = 0.5))

to_plot <- gen_div %>%
    select(Group,He_harm,He_harm2,He_harm3) %>%
    filter(Group %in% c("Mammal","Fish","Plant", "Bird")) %>%
    gather(metric, value, -Group)

ggplot(to_plot, aes(value, fill = Group, color = Group))+
    geom_density(alpha = .3)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    facet_wrap(.~metric, scales = "free")

14.4 High He with latitude?

14.4.1 Absolution latitude

toplot <- gen_div %>%
    mutate(Hemisphere = ifelse(lat<0, "South", "North"),
           lat_abs = abs(lat),
           He_harm = scale(He_harm),
           He_harm3 = scale(He_harm3))

ggplot(toplot, aes(x = lat_abs, y = He_harm, color = Group))+
    geom_point(alpha = .1)+
    stat_smooth(method = "lm")+
    facet_wrap(.~Group)

## `geom_smooth()` using formula = 'y ~ x'

mod1 <- lm(He_harm~lat*Group, toplot)
emtrends(mod1, ~ lat*Group, var = "lat")

##   lat Group     lat.trend       SE   df lower.CL upper.CL
##  46.9 Amphibian  -0.06867 0.029720 7833 -0.12693 -0.01041
##  46.9 Bird        0.00394 0.001394 7833  0.00121  0.00668
##  46.9 Fish        0.01032 0.000837 7833  0.00868  0.01196
##  46.9 Mammal     -0.00464 0.004656 7833 -0.01377  0.00449
##  46.9 Molluscs    0.07562 0.186554 7833 -0.29008  0.44131
##  46.9 Plant      -0.01902 0.001150 7833 -0.02128 -0.01677
##  46.9 Reptile     0.08038 0.023489 7833  0.03433  0.12642
## 
## Confidence level used: 0.95

14.5 Spatial pattern

mundi <- terra::vect(rnaturalearth::ne_coastline(scale = 110, returnclass = "sp"))

## Warning: The `returnclass` argument of `ne_download()` sp as of rnaturalearth 1.0.0.
## ℹ Please use `sf` objects with {rnaturalearth}, support for Spatial objects
##   (sp) will be removed in a future release of the package.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

# get vect gen div
vect_div <- terra::vect(gen_div,geom=c("long","lat"),crs=crs(mundi))

# get vect centroids bioshifts
vect_biov1 <- terra::vect(biov1 %>%
                              filter(spp %in% gen_div$spp),
                          geom=c("centroid.x","centroid.y"),crs=crs(mundi))

# get raster bioshifts shp files study areas
get_raster_bioshifts = "NO"
if(get_raster_bioshifts=="YES"){
    my_ext = terra::ext(mundi)
    my_crs = crs(mundi)
    rast_biov1 <- terra::rast(my_ext, crs = my_crs, res = 0.5)
    values(rast_biov1) <- 0
    
    fgdb <- "C:/Users/brunn/NextCloud/Bioshifts/Study_Areas.gdb"
    fc_list <- ogrListLayers(fgdb)
    fc_list <- fc_list[which(fc_list %in% unique(vect_biov1$ID))]
    
    for(i in 1:length(fc_list)){ cat("\r",i,"from",length(fc_list))
        tmp = terra::vect(sf::st_read(fgdb, layer=fc_list[i]))
        tmp = terra::cells(rast_biov1,tmp)
        tmp_cell = tmp[,2]
        tmp_vals = rast_biov1[tmp_cell][,1]
        rast_biov1[tmp_cell] = tmp_vals+1
    }
    names(rast_biov1) <- "SA"
    rast_biov1[rast_biov1==0] <- NA
    
    writeRaster(rast_biov1, "Data/raster_bioshifts_SA.tif")
    
} else {
    rast_biov1 <- terra::rast("Data/raster_bioshifts_SA.tif")
}

values_biov1 <- na.omit(values(rast_biov1))

ggplot()+
    ggtitle("Genetic diversity data")+
    geom_spatvector(data=mundi)+
    geom_spatvector(data=vect_div,aes(color = Marker))+
    theme_blank()

ggplot()+
    ggtitle("Genetic diversity data")+
    geom_spatvector(data=mundi)+
    geom_spatvector(data=vect_div,aes(color = Source))+
    theme_blank()

ggplot()+
    ggtitle("Bioshifts data \n(Centroid of study areas)")+
    geom_spatvector(data=mundi)+
    geom_spatvector(data=vect_biov1, aes(color = ECO))+
    theme_blank()

ggplot()+
    ggtitle("Bioshifts data \n(Overlap study areas)")+
    geom_spatraster(data=rast_biov1)+
    scale_fill_whitebox_b(
        palette = "muted",
        na.value = "white",
        breaks = seq(min(values_biov1), max(values_biov1), 1))+
    geom_spatvector(data=vect_biov1, aes(color = ECO))+
    geom_spatvector(data=mundi)+
    theme_blank()

15 Phenotypic rates

pheno_rate <- pheno %>%
    mutate(long = as.numeric(sample1.longitude),
           lat = as.numeric(sample1.latitude),
           trait1 = as.numeric(mean1),
           trait2 = as.numeric(mean2),
           spp = spp_new,
           Trait = trait_type, 
           Years = as.numeric(years),
           Gen = as.numeric(generations)) %>% 
    dplyr::select(spp, long, lat, Trait, trait1, trait2, Years, Gen) %>%
    dplyr::filter(!is.na(trait1) & !is.na(trait2))
pheno_rate$Source = "PROCEED"

15.1 Calculate rate & harmozine

for(i in 1:nrow(pheno_rate)){
    sppi <- pheno_rate$spp[i]
    traiti <- pheno_rate$Trait[i]
    pos_spp_trait_i <- which(pheno_rate$spp == sppi & pheno_rate$Trait == traiti)
    pheno_rate$PR1[i] <- (pheno_rate$trait2[i] - pheno_rate$trait1[i]) / mean(c(pheno_rate$trait2[pos_spp_trait_i], pheno_rate$trait1[pos_spp_trait_i])) * pheno_rate$Years[i]
    pheno_rate$PR2[i] <- (pheno_rate$trait2[i] - pheno_rate$trait1[i]) / sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i], pheno_rate$trait1[pos_spp_trait_i])))^2 * pheno_rate$Years[i]
}

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced

pheno_rate$PR_harm <- NA

m <- unique(pheno_rate$Trait)
for(i in 1:length(m)){
    pos <- which(pheno_rate$Trait == m[i])
    # DeKort
    p <- scale(pheno_rate$PR1[pos])
    p <- (p - min(p, na.rm = T))/(max(p, na.rm = T)-min(p, na.rm = T))
    pheno_rate$PR_harm[pos] <-  p
}

# gen_div %>%
# group_by(spp) %>%
# tally() %>%
# summary()

15.2 Distribution of pheno rates

# Look into phenotic rates
ggplot(pheno_rate, aes(PR1)) +
    geom_histogram() +
    facet_wrap(Trait~., scales = "free") +
    ggtitle("Raw data")

## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).

ggplot(pheno_rate, aes(PR_harm)) +
    geom_histogram() +
    facet_wrap(Trait~., scales = "free") +
    ggtitle("Harmonization following De Kort")

## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).

16 Merge genetic and bioshifts data

We tried several methods for merging genetic data and bioshifts.

• Method 1: Normal merge Uses the merge function to aggregate bioshifts and gen div data. This creates multiple false duplicates.

• Method 2: Median He per species. This ignores spatial proximity of He values to range shift values.

• Method 3: Median He per Param & species Use lower latitude TE and higher latitude as LE (North Hemisphere). Although this collects He values based on latitude, the proximity of He to the study areas where range shifts were observed is ignored.

• Method 4: Distance based approach. Calculates distance weighted mean of He to the centroid of the study area.

• Method 5: Location based approach. Get the closest He to the centroid (for centroid shift), to the max latitude (for LE shift, at the North hemisphere), and to the min latitude (for TE shift, at the North hemisphere) of the study area.

We decided that Method 5 is the best one.

17 Method 1 - Normal merge

This is not the correct way to merge because it creates multiple false duplicates.

18 Method 2 - Median He per species

This ignores spatial proximity of He values to range shift values.

gen_div_avg <- gen_div %>%
    group_by(spp) %>%
    dplyr::summarise(N = length(spp),
                     He_harm = median(He_harm),
                     He_harm2 = median(He_harm2),
                     He_harm3 = median(He_harm3)) 

#merge all data frames in list
gen_data_v1_avg <- append(list(gen_div_avg), list(biov1)) %>% reduce(full_join, by='spp')
gen_data_v1_avg <- gen_data_v1_avg %>% dplyr::filter(!is.na(He_harm), !is.na(SHIFT))

# N species
length(unique(gen_data_v1_avg$spp))

## [1] 304

18.1 Save data

write.csv(gen_data_v1_avg,"Data/gen_data_final_m2.csv",row.names = FALSE)

19 Method 3 - Median He per Param & species

Use lower latitude TE and higher latitude as LE (North Hemisphere).
Although this collects He values based on latitude, the proximity of He to the study areas where range shifts were observed is ignored.

20 Method 4 - Based on distance

Calculate distance weighted mean He for each species in the bioshifts database.
This method collects He values more close to the centroid of the study area.

21 Method 5 - Based on location

Get the closest He to the centroid (for O), to the max latitude (for LE, at the North hemisphere), and to the min latitude (for TE, at the North hemisphere) of the study area.

# sp list
m <- unique(biov1$spp[which(biov1$spp %in% gen_div$spp)])

gen_data_v1_dist2 <- data.frame()

# sp2go="Alces alces"
# sp2go="Sylvia atricapilla"
for(i in 1:length(m)){ cat(i, "from", length(m), "\r")
    sp2go <- m[i]
    
    # subset genetic data
    sub_gen <- subset(gen_div[,1:9], spp == sp2go)
    sub_gen_v <- terra::vect(sub_gen, 
                             geom=c("long", "lat"), 
                             crs = "+proj=longlat +datum=WGS84 +no_defs")
    
    # subset bioshifts data
    sub_bio <- subset(biov1, spp == sp2go)
    
    # study areas for species i
    areas <- unique(sub_bio$ID)
    
    gen_data_v1_dist2_tmp <- data.frame()
    
    # get genetic data for species i in each study area
    for(j in 1:nrow(sub_bio)){
        
        # bioshifts in study area j and range pos r
        sub_bio_area_j_tmp <- sub_bio[j,]
        
        range_pos2go <- sub_bio_area_j_tmp$Param
        
        coord_names <- NA
        coord_names <- if(range_pos2go == "O"){ c("centroid.x", "centroid.y") } else {coord_names}
        coord_names <- if(range_pos2go == "LE" & sub_bio_area_j_tmp$centroid.y > 0){ c("max_lat.x", "max_lat.y") } else {coord_names}
        coord_names <- if(range_pos2go == "LE" & sub_bio_area_j_tmp$centroid.y < 0){ c("min_lat.x", "min_lat.y") } else {coord_names}
        coord_names <- if(range_pos2go == "TE" & sub_bio_area_j_tmp$centroid.y < 0){ c("max_lat.x", "max_lat.y") } else {coord_names}
        coord_names <- if(range_pos2go == "TE" & sub_bio_area_j_tmp$centroid.y > 0){ c("min_lat.x", "min_lat.y") } else {coord_names}
        
        sub_bio_area_j_v = data.frame(sub_bio_area_j_tmp)[,coord_names]
        names(sub_bio_area_j_v) <- c("long", "lat")
        
        sub_bio_area_j_v <- terra::vect(unique(sub_bio_area_j_v[,c("long", "lat")]), 
                                        geom=c("long", "lat"), 
                                        crs = "+proj=longlat +datum=WGS84 +no_defs")
        
        # plot(terra::vect(rbind(sub_gen[,c("long", "lat")], sub_bio_area_j_tmp[,c("long", "lat")]),
        #  geom=c("long", "lat"),
        #  crs = "+proj=longlat +datum=WGS84 +no_defs"))
        # plot(sub_bio_area_j_v, col = "red", add=T)
        
        dists <- terra::distance(sub_bio_area_j_v, sub_gen_v)[1,]
        dists_order <- order(dists)[1]
        min_dist <- dists[dists_order] # distance to closest
        
        # the closest
        sub_gen_tmp <- sub_gen[dists_order,]
        sub_gen_tmp$min_dist <- min_dist
        
        # weighted distance
        sub_gen_tmp$He_harm_w <- weighted.mean(sub_gen$He_harm, 1/(dists^2)) # inverse of the distance
        sub_gen_tmp$He_harm2_w <- weighted.mean(sub_gen$He_harm2, 1/(dists^2)) # inverse of the distance
        sub_gen_tmp$He_harm3_w <- weighted.mean(sub_gen$He_harm3, 1/(dists^2)) # inverse of the distance
        
        gen_data_v1_dist2_tmp <- rbind(gen_data_v1_dist2_tmp,
                                       append(list(sub_gen_tmp), 
                                              list(sub_bio_area_j_tmp)) %>%
                                           reduce(full_join, by="spp"))
        
    }
    
    gen_data_v1_dist2 <- rbind(gen_data_v1_dist2,
                               gen_data_v1_dist2_tmp)
    
}

gen_data_v1_dist2 <- gen_data_v1_dist2 %>% dplyr::filter(!is.na(He), !is.na(SHIFT))

21.1 Save data

write.csv(gen_data_v1_dist2,"Data/gen_data_final_m5.csv",row.names = FALSE)

22 Explore data

# n range shifts
nrow(gen_data_v1_dist2)

## [1] 1881

# n genetic diversity measurements (after averaging by location)
dim(gen_div)

## [1] 7847   17

22.1 The closest He vs distance weighted He

plot(gen_data_v1_dist2$He_harm,gen_data_v1_dist2$He_harm_w)

plot(gen_data_v1_dist2$He_harm2,gen_data_v1_dist2$He_harm2_w)

plot(gen_data_v1_dist2$He_harm3,gen_data_v1_dist2$He_harm3_w)

22.2 Which species?

# N species
length(unique(gen_data_v1_dist2$spp))

## [1] 304

# Use only plus plus or minus minus
table(gen_data_v1_dist2$shift_vel_sign)

## 
## negneg negpos posneg pospos 
##     36    793     63    989

# N species
length(unique(gen_data_v1_dist2$spp))

## [1] 304

toplot_ <- gen_data_v1_dist2 %>%
    group_by(Group) %>%
    dplyr::summarise(Species = length(unique(spp)),
                     Obs = length(spp))

toplot_ <- reshape2::melt(toplot_)

## Using Group as id variables

ggplot(toplot_, aes(x = Group, y = value))+
    geom_bar(stat="identity")+
    geom_text(aes(y=value, label=value), 
              hjust = -.1, vjust=0.2, size=3, 
              position = position_dodge(0.9))+
    theme_classic()+
    coord_flip()+
    facet_wrap(.~variable, scales = "free", ncol = 1)

22.3 Compare lag values of the full bioshifts vs the subset (genetic) dataset

toplot <- rbind(
    data.frame(dataset = "Full", 
               lags = biov1$lag2, 
               shifts = biov1$SHIFT),
    data.frame(dataset = "Subset", 
               lags = gen_data_v1_dist2$lag2, 
               shifts = gen_data_v1_dist2$SHIFT))

# lags
ggplot(toplot, aes(x = dataset, y=lags, fill = dataset, color = dataset))+
    geom_boxplot(alpha = .5, outlier.shape = NA)+
    scale_y_continuous(limits = quantile(toplot$lags, c(0.1, 0.9), na.rm = T))

## Warning: Removed 6280 rows containing non-finite values (`stat_boxplot()`).

tapply(toplot$lags, toplot$dataset, summary)

## $Full
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -145.1653   -0.4203    0.5907    0.3758    2.1868   13.1259 
## 
## $Subset
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -38.3203   0.0271   1.0197   0.9206   2.3096  13.1259

mod1 <- aov(lags~dataset, data = toplot)
summary(mod1)

##                Df Sum Sq Mean Sq F value   Pr(>F)    
## dataset         1    525   525.5   24.45 7.66e-07 ***
## Residuals   31936 686320    21.5                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# shift
ggplot(toplot, aes(x = dataset, y=shifts, fill = dataset, color = dataset))+
    geom_boxplot(alpha = .5, outlier.shape = NA)+
    scale_y_continuous(limits = quantile(toplot$shifts, c(0.1, 0.9), na.rm = T))

## Warning: Removed 6376 rows containing non-finite values (`stat_boxplot()`).

tapply(toplot$shifts, toplot$dataset, summary)

## $Full
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -119.1696   -0.4356    0.2908    1.1661    2.4130  146.3000 
## 
## $Subset
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -119.1696   -0.3667    0.1429    0.9984    1.8912   39.3400

mod1 <- aov(shifts~dataset, data = toplot)
summary(mod1)

##                Df Sum Sq Mean Sq F value Pr(>F)
## dataset         1     50   49.78   1.694  0.193
## Residuals   31936 938576   29.39

There are difference in mean values of lags, but no difference in mean shift values, between the full and subset datasets.

22.4 Lag

22.4.1 General pattern

22.4.1.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = lagC))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3, aes(color = SHIFT_abs))+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.1.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.1.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.2 By Param

22.4.2.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.2.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.2.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.3 By Group

22.4.3.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'

## Warning in qt((1 - level)/2, df): NaNs produced

## `geom_smooth()` using formula = 'y ~ x'

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.3.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'

## Warning in qt((1 - level)/2, df): NaNs produced

## `geom_smooth()` using formula = 'y ~ x'

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.3.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'

## Warning in qt((1 - level)/2, df): NaNs produced

## `geom_smooth()` using formula = 'y ~ x'

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.4 By Group * Param

22.4.4.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.4.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.4.4.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5 Lag 2

22.5.1 General pattern

22.5.1.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.1.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.1.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.2 By Param

22.5.2.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.2.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.2.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.3 By Group

22.5.3.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'

## Warning in qt((1 - level)/2, df): NaNs produced

## `geom_smooth()` using formula = 'y ~ x'

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.3.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'

## Warning in qt((1 - level)/2, df): NaNs produced

## `geom_smooth()` using formula = 'y ~ x'

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.3.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'

## Warning in qt((1 - level)/2, df): NaNs produced

## `geom_smooth()` using formula = 'y ~ x'

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.4 By Group * Param

22.5.4.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.4.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.5.4.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("lagC (Velocity - Shift)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6 SHIFT

22.6.1 General pattern

22.6.1.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.1.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.1.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.2 By Param

22.6.2.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.2.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = log1p(sqrt(abs(SHIFT_cor))), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = log1p(sqrt(abs(SHIFT_cor))), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.2.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Param, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.3 By Group

22.6.3.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'

## Warning in qt((1 - level)/2, df): NaNs produced

## `geom_smooth()` using formula = 'y ~ x'

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.3.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'

## Warning in qt((1 - level)/2, df): NaNs produced

## `geom_smooth()` using formula = 'y ~ x'

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.3.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'

## Warning in qt((1 - level)/2, df): NaNs produced

## `geom_smooth()` using formula = 'y ~ x'

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(.~Group, scales = "free", ncol = 3)

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.4 By Group * Param

22.6.4.1 He_harm

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm, y = (abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm, y = (abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.4.2 He_harm2

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

22.6.4.3 He_harm3

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "LAT") %>%
    ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Latitude")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

gen_data_v1_dist2 %>%
    dplyr::filter(Type == "ELE") %>%
    ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
    ggtitle("Elevation")+
    xlab("Genetic diversity (He harm logit)")+
    ylab("Range shift (km/year)") +
    scale_color_viridis_c(direction = -1)+
    geom_point(alpha = .5, size = 3)+
    theme_bw()+
    geom_smooth(method = "lm", color = "black", size=1)+
    stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE, 
                aes(by=as.factor(Article_ID)), color = "black")+
    facet_wrap(Param~Group, scales = "free")

## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'